import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def      __lowercase				(      _UpperCAmelCase			)      ->     Union[str, Any]:






  '''simple docstring'''

  __lowercase					=		os.path.join(args.tf_model_dir			, "parameters.json"			)
  __lowercase					=		json.loads(open(_lowercase			).read()			)
  if not params:
    raise ValueError(
        f'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.'''			)
  if not args.output.endswith(".pt"			):
    __lowercase					=		args.output + ".pt"
  __lowercase					=		OrderedDict()
  with tf.device("/CPU:0"			):
    __lowercase					=		tf.train.load_checkpoint(args.tf_model_dir			)
    __lowercase					=		reader.get_variable_to_shape_map()
    for key_name in shapes.keys():
      __lowercase					=		reader.get_tensor(_lowercase			).astype(np.floataa			)
      if key_name.endswith("/adam_m"			) or key_name.endswith("/adam_v"			):
        continue
      if key_name.startswith("pasts/"			):
        if key_name.startswith("pasts/mlp"			):
          __lowercase					=		int(key_name[9]			)
        elif key_name.startswith("pasts/out"			):
          __lowercase					=		8
        __lowercase					=		"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
        __lowercase					=		vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
        __lowercase					=		torch.tensor(_lowercase			)
      elif key_name.startswith("model/moe"			):
        __lowercase					=		int(key_name[9:].split("/"			)[0]			)
        if key_name.endswith("/switch_gating/kernel"			):
          __lowercase					=		"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
          __lowercase					=		vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
          __lowercase					=		torch.tensor(_lowercase			)
        elif key_name.endswith("/softmlp/kernel"			):
          __lowercase					=		"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
          __lowercase					=		vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
          __lowercase					=		torch.tensor(_lowercase			)
        elif key_name.endswith("/wo/kernel"			) or key_name.endswith("/wi/kernel"			):
          __lowercase					=		key_name[-9:-7]
          for i in range(16			):
            __lowercase					=		"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
            __lowercase					=		(
                vnp[i].transpose([1, 0]			).copy()
            )  # In Mesh-Tensorflow, it is one array, so it is divided
            __lowercase					=		torch.tensor(_lowercase			)
      elif key_name.startswith("model/mlp"			):
        __lowercase					=		int(key_name[9:].split("/"			)[0]			)
        if key_name.endswith("/p1/kernel"			):
          __lowercase					=		"model.blocks.%d.feed_forward.mlp.wi.weight" % player
          __lowercase					=		vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
          __lowercase					=		torch.tensor(_lowercase			)
        elif key_name.endswith("/p1/bias"			):
          __lowercase					=		"model.blocks.%d.feed_forward.mlp.wi.bias" % player
          __lowercase					=		vnp.copy()  # same because it is one dimensional
          __lowercase					=		torch.tensor(_lowercase			)
        elif key_name.endswith("/p2/kernel"			):
          __lowercase					=		"model.blocks.%d.feed_forward.mlp.wo.weight" % player
          __lowercase					=		vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
          __lowercase					=		torch.tensor(_lowercase			)
        elif key_name.endswith("/p2/bias"			):
          __lowercase					=		"model.blocks.%d.feed_forward.mlp.wo.bias" % player
          __lowercase					=		vnp.copy()  # same because it is one dimensional
          __lowercase					=		torch.tensor(_lowercase			)
      elif key_name.startswith("model/ln"			):
        __lowercase					=		int(key_name[8:].split("/"			)[0]			)
        if key_name.endswith("/b"			):
          __lowercase					=		"model.blocks.%d.feed_forward.norm.bias" % player
          __lowercase					=		vnp.copy()  # same because it is one dimensional
          __lowercase					=		torch.tensor(_lowercase			)
        elif key_name.endswith("/g"			):
          __lowercase					=		"model.blocks.%d.feed_forward.norm.weight" % player
          __lowercase					=		vnp.copy()  # same because it is one dimensional
          __lowercase					=		torch.tensor(_lowercase			)
      elif key_name.startswith("model/att"			):
        __lowercase					=		int(key_name[9:].split("/"			)[0]			)
        if key_name.endswith("/qkv/kernel"			):
          __lowercase					=		vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
          __lowercase					=		state[:, 0, :, :]
          __lowercase					=		state[:, 1, :, :]
          __lowercase					=		state[:, 2, :, :]
          __lowercase					=		(
              state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]			)
              .transpose([1, 0]			)
              .copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          __lowercase					=		(
              state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]			)
              .transpose([1, 0]			)
              .copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          __lowercase					=		(
              state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]			)
              .transpose([1, 0]			)
              .copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          __lowercase					=		"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
          __lowercase					=		torch.tensor(_lowercase			)
          __lowercase					=		"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
          __lowercase					=		torch.tensor(_lowercase			)
          __lowercase					=		"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
          __lowercase					=		torch.tensor(_lowercase			)
        elif key_name.endswith("/o/kernel"			):
          __lowercase					=		"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
          __lowercase					=		(
              vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]			).transpose([1, 0]			).copy()
          )  # Mesh-Tensorflow is a diagonal matrix
          __lowercase					=		torch.tensor(_lowercase			)
      elif key_name.startswith("model/an"			):
        __lowercase					=		int(key_name[8:].split("/"			)[0]			)
        if key_name.endswith("/b"			):
          __lowercase					=		"model.blocks.%d.self_attn.norm.bias" % player
          __lowercase					=		vnp.copy()  # same because it is one dimensional
          __lowercase					=		torch.tensor(_lowercase			)
        elif key_name.endswith("/g"			):
          __lowercase					=		"model.blocks.%d.self_attn.norm.weight" % player
          __lowercase					=		vnp.copy()  # same because it is one dimensional
          __lowercase					=		torch.tensor(_lowercase			)
      elif (
          key_name.startswith("model/wte"			)
          or key_name.startswith("model/wpe"			)
          or key_name.startswith("model/ete"			)
      ):
        __lowercase					=		{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
            key_name[-3:]
        ]
        __lowercase					=		"model.%s.weight" % nlayer
        __lowercase					=		vnp.copy()  # same in embedded
        __lowercase					=		torch.tensor(_lowercase			)
        if key_name.startswith("model/wte"			):
          __lowercase					=		"lm_head.weight"
          __lowercase					=		vnp.copy()  # same in embedded
          __lowercase					=		torch.tensor(_lowercase			)
      elif key_name.startswith("model/wob"			):
        __lowercase					=		"final_logits_bias"
        __lowercase					=		vnp.copy()  # same in embedded
        __lowercase					=		state.reshape((1, -1)			)
        __lowercase					=		torch.tensor(_lowercase			)
      elif key_name == "model/dense/kernel":
        __lowercase					=		"model.last_project.weight"
        __lowercase					=		vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
        __lowercase					=		torch.tensor(_lowercase			)
      elif key_name == "model/dense_1/bias":
        __lowercase					=		"model.last_project.bias"
        __lowercase					=		vnp.copy()  # same because it is one dimensional
        __lowercase					=		torch.tensor(_lowercase			)
  torch.save(_lowercase			, args.output			)


if __name__ == "__main__":
     lowerCAmelCase__	   =						argparse.ArgumentParser(
         description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
     )
     parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
     parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
     lowerCAmelCase__	   =						parser.parse_args()
     convert_tf_gptsan_to_pt(args)





"""simple docstring"""



from collections.abc import Sequence
from queue import Queue







class    __a						:







				def __init__( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase=None      ,	UpperCAmelCase=None   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			start
					lowerCAmelCase_      							=			end
					lowerCAmelCase_      							=			val
					lowerCAmelCase_      							=			(start + end) // 2
					lowerCAmelCase_      							=			left
					lowerCAmelCase_      							=			right







				def __repr__( self   ):

					'''simple docstring'''






					return F"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""







class    __a						:







				def __init__( self      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			collection
					lowerCAmelCase_      							=			function
					if self.collection:
						lowerCAmelCase_      							=			self._build_tree(0      ,	len(UpperCAmelCase   ) - 1   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					self._update_tree(self.root      ,	UpperCAmelCase      ,	UpperCAmelCase   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					return self._query_range(self.root      ,	UpperCAmelCase      ,	UpperCAmelCase   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					if start == end:
						return SegmentTreeNode(UpperCAmelCase      ,	UpperCAmelCase      ,	self.collection[start]   )
					lowerCAmelCase_      							=			(start + end) // 2
					lowerCAmelCase_      							=			self._build_tree(UpperCAmelCase      ,	UpperCAmelCase   )
					lowerCAmelCase_      							=			self._build_tree(mid + 1      ,	UpperCAmelCase   )
					return SegmentTreeNode(UpperCAmelCase      ,	UpperCAmelCase      ,	self.fn(left.val      ,	right.val   )      ,	UpperCAmelCase      ,	UpperCAmelCase   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					if node.start == i and node.end == i:
						lowerCAmelCase_      							=			val
						return
					if i <= node.mid:
						self._update_tree(node.left      ,	UpperCAmelCase      ,	UpperCAmelCase   )
					else:
						self._update_tree(node.right      ,	UpperCAmelCase      ,	UpperCAmelCase   )
					lowerCAmelCase_      							=			self.fn(node.left.val      ,	node.right.val   )







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					if node.start == i and node.end == j:
						return node.val

					if i <= node.mid:
						if j <= node.mid:
							# range in left child tree
							return self._query_range(node.left      ,	UpperCAmelCase      ,	UpperCAmelCase   )
						else:
							# range in left child tree and right child tree
							return self.fn(
							    self._query_range(node.left      ,	UpperCAmelCase      ,	node.mid   )      ,	self._query_range(node.right      ,	node.mid + 1      ,	UpperCAmelCase   )      ,	)
					else:
						# range in right child tree
						return self._query_range(node.right      ,	UpperCAmelCase      ,	UpperCAmelCase   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					if self.root is not None:
						lowerCAmelCase_      							=			Queue()
						queue.put(self.root   )
						while not queue.empty():
							lowerCAmelCase_      							=			queue.get()
							yield node

							if node.left is not None:
								queue.put(node.left   )

							if node.right is not None:
								queue.put(node.right   )


if __name__ == "__main__":
		import operator

		for fn in [operator.add, max, min]:
				print('*' * 50)
				lowercase_							=							SegmentTree([2, 1, 5, 3, 4], fn)
				for node in arr.traverse():
						print(node)
				print()

				arr.update(1, 5)
				for node in arr.traverse():
						print(node)
				print()

				print(arr.query_range(3, 4))  # 7
				print(arr.query_range(2, 2))  # 5
				print(arr.query_range(1, 3))  # 13
				print()